The reconstruction of weather data is essential for various applications such as weather forecasting, climate research, and disaster preparedness. Traditionally, this task required multiple instruments to record different attributes, posing challenges for complete data reconstruction. In this study, we have proposed a simple yet effective approach based on graph embedding and independent features to reconstruct the entire family of weather attributes. Exploiting weather histories from 62 stations across diverse climate regions in Nepal, our method enables the imputation of temperature and humidity data for specific weather stations as well as all stations over a period of time. Rigorous testing and validation demonstrate the effectiveness of our approach, with key evaluation metrics including Mean Squared Error (MSE), Mean Absolute Error (MAE), and coefficient of determination (R2). Our results highlight the model’s proficiency in reconstructing comprehensive weather data, offering a promising avenue for enhancing the reliability of weather-related applications. Also, the use of graph embedding techniques and independent features, our approach provides a robust framework for reconstructing historical weather data, addressing the challenges associated with incomplete or fragmented datasets.
목차
Abstract 1. Introduction 2. Methodology 3. Data Collection and Preprocessing 4. Experiment and Evaluation 5. Results and Discussions 6. Conclusion References
한국AI디지털융합학회(구 한국디지털융합학회) [The Korean Academic Society of AI Digital Convergence]
설립연도
2015
분야
사회과학>경영학
소개
본 학회는 디지털 경영에 관련된 디지털 미디어, 디지털 통신, 디지털 방송, 디지털 콘텐츠, 디지털 문화, 디지털 사회, 디지털 유통, 디지털 금융, 디지털 물류, 디지털 정책, 디지털 기술, 디지털 교육 그리고 디지털과 아날로그의 비교 등에 대한 학제간 연구와 실사구시적인 적용을 통하여 디지털 경영의 발전과 한국이 세계적인 디지털 강국으로 성장하기 위한 학술적인 기반과 실무적인 지침을 조성하는 것을 목적으로 하고 있습니다.
간행물
간행물명
IJICTDC [International Journal of Information Communication Technology and Digital Convergence]